Added a code chunk to load the gapminder package. It appears after the preamble include and will load the library when the document is rendered. The chunk is configured to echo the code and evaluate it.
ImportantPrompt
Using package plotly, build an animated plot (each frame corresponding to a value in colum Year) mapping column gdpPercap to X-axis and lifeExpectancy to Y-axis
NoteAnswer
Creating an animated plotly plot. Checking the current file and the gapminder dataset structure.
Adding plotly and creating the animated plot
Code
# Create animated plotly visualization# X-axis: gdpPercap, Y-axis: lifeExp, Frame: year(gapminder|>ggplot()+aes( x =gdpPercap, y =lifeExp, frame =year, color =continent, size =pop, text =country)+geom_point(alpha =0.6)+scale_x_log10()+scale_size_area( max_size =15, labels =scales::label_number(scale =1/1e6, suffix =" M"), guide =guide_legend(title ="Population"))+guides(color ="none")+labs( x ="GDP per Capita", y ="Life Expectancy", title ="Gapminder: Life Expectancy vs GDP per Capita (Animated)"))|>ggplotly(height =500, width =750)
Added an animated plotly visualization:
Loads required packages: plotly and ggplot2 (in addition to gapminder)
Creates the animated plot with:
X-axis: gdpPercap (GDP per capita)
Y-axis: lifeExp (Life expectancy)
Frame: year (enables animation by year)
Log scale on the x-axis (common for GDP)
Labels and title
The frame = year aesthetic in the aes() call makes plotly animate by year. When rendered, you’ll see a play button to animate through the years, showing how countries move across the GDP per capita vs. life expectancy space over time.
Second thoughts
Metadata (FAIRness)
Commenting
Spelling out the message
Data criticism
Improving accessibility
Information released after hovering
Navigating the plot
Using plotly
The case for interactive dashboards
shiny
dash
Accessing gapminder metadata
ImportantPrompt
Where can I find metadata concerning the gapminder dataset?
NoteAnswer
Metadata about the gapminder dataset can be found in several places:
1. Package documentation
The most direct way to access metadata is through R’s help system:
Code
# View the dataset documentation?gapminder# Or equivalently# help(gapminder)
2. Package vignettes
The gapminder package may include vignettes with detailed information:
Code
# List available vignettesbrowseVignettes("gapminder")# Or view a specific vignettevignette("gapminder", package ="gapminder")
3. Dataset structure and attributes
You can inspect the dataset structure and any stored metadata:
Package: gapminder
Title: Data from Gapminder
Version: 1.0.0
Authors@R: person("Jennifer", "Bryan", , "jenny@posit.co", role =
c("aut", "cre"), comment = c(ORCID = "0000-0002-6983-2759"))
Description: An excerpt of the data available at Gapminder.org. For
each of 142 countries, the package provides values for life
expectancy, GDP per capita, and population, every five years,
from 1952 to 2007.
License: CC0
URL: https://github.com/jennybc/gapminder,
https://www.gapminder.org/data/,
https://doi.org/10.5281/zenodo.594018,
https://jennybc.github.io/gapminder/
BugReports: https://github.com/jennybc/gapminder/issues
Depends: R (>= 3.1.0)
Imports: tibble
Suggests: dplyr, ggplot2, knitr, rmarkdown, testthat (>= 3.0.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-03-09 18:35:33 UTC; jenny
Author: Jennifer Bryan [aut, cre]
(<https://orcid.org/0000-0002-6983-2759>)
Maintainer: Jennifer Bryan <jenny@posit.co>
Repository: CRAN
Date/Publication: 2023-03-10 09:50:08 UTC
Built: R 4.4.0; ; 2024-05-30 14:53:13 UTC; unix
RemoteType: standard
RemotePkgRef: gapminder
RemoteRef: gapminder
RemoteRepos: https://cloud.r-project.org
RemotePkgPlatform: source
RemoteSha: 1.0.0
-- File: /home/boucheron/R/x86_64-pc-linux-gnu-library/4.4/gapminder/Meta/package.rds
To cite package 'gapminder' in publications use:
Bryan J (2023). _gapminder: Data from Gapminder_. R package version
1.0.0, <https://CRAN.R-project.org/package=gapminder>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {gapminder: Data from Gapminder},
author = {Jennifer Bryan},
year = {2023},
note = {R package version 1.0.0},
url = {https://CRAN.R-project.org/package=gapminder},
}
This answer is mildly satisfactory. But what are we expecting concerning metadata?